Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments

Martini, B.; Gharbaoui, M.; Castoldi, P.

doi:10.1016/j.future.2022.12.033

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…With the evolution of networks toward beyond 5G, the complexity of this task tends to increase [99]. Consequently, an intent layer will be required to translate service profiles into slice requirements [100].…”

Section: A Intelligent Translation For Ns Requirementsmentioning

confidence: 99%

“…Addressing this goal, however, poses several challenges, among them, defining rich semantics to express the intent of verticals [102]. Although the integration with AI technologies, and Natural Language Processing (NLP) in particular, can bridge this gap, those technologies are still at their early stage and require further research efforts before being integrated into the network slice LCM [100]. This research gap can be evinced in our survey, where only one work [25] uses ML to bridge this gap.…”

Section: A Intelligent Translation For Ns Requirementsmentioning

confidence: 99%

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Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Donatti¹,

Corrêa

Martins

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

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Section: A Intelligent Translation For Ns Requirementsmentioning

confidence: 99%

Section: A Intelligent Translation For Ns Requirementsmentioning

confidence: 99%

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Donatti¹,

Corrêa

Martins

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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show abstract

“…Several solutions are available to address the issue of slow internet speeds in incident response. One solution is to use software-defined networking (SDN), which can improve the network efficiency, flexibility, and scalability for the IoT devices [ 4 ]. SDN allows for the centralized management of network resources, which can lead to a more efficient use of the available bandwidth [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology

Razaque,

Yoo,

Bektemyssova

et al. 2023

Sensors

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Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

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Analysis of Power Consumption After Switching to 5G

Makhlouf,

Mazri

2024

Lecture Notes in Networks and Systems

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Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments

Cited by 10 publications

References 43 publications

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Survey on Machine Learning-Enabled Network Slicing: Covering the Entire Life Cycle

Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology

Analysis of Power Consumption After Switching to 5G

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